Building Integrated Photovoltaics 4.0: Digitization of the Photovoltaic Integration in Buildings for a Resilient Infra at Large Scale
- A brief overview of the BIPV system along with different types of integration of PV modules with building envelopes represented pictorially.
- A discussion of various issues related to the performance of the BIPV system. The issues include the rise in PV modules’ temperature, different types of faults in PV modules, accumulation of dust particles over the modules, etc.
- A discussion of the significance and application of the digital technologies implementation for overcoming the different issues related to the BIPV system with the proposed architecture.
- Vital recommendations including ML and DL for image enhancement for flaws detection in real-time image data; edge computing to implement DL for intelligent BIPV data analytics; fog computing for 6G assisted IoT network in BIPV; edge computing integration in UAV for intelligent automation and detection; augmented reality, virtual reality, and digital twins for virtual BIPV system as a part of future directions. Research challenges are also discussed.
2. Overview of BIPV System
- BIPV roof
- BIPV facade
- STPV glazing/windows
Issues/Challenges in the BIPV Systems
- The system attains a high temperature during the sunshine hours, which reduces power generation. It also affects the building envelope temperature and the building thermal comfort. For preventing the heating of the modules, an uncontrolled high-power forced ventilation is used, which consumes much energy leading to an overall reduction in system efficiency.
- In a large BIPV system, different types of optical, electrical, and non-classified faults occur, depending on the ambient condition, type of integration, and adjacent shadings on buildings that need to be analyzed regularly.
- Regular and safe cleaning of dust-accumulated PV modules in large buildings, roofs, facades, and fenestration is challenging, which reduces the overall efficiency of the BIPV system and daylight availability in buildings.
3. Digitalization for BIPV
- IoT: IoT was first proposed at the end of the twentieth century, and it was developed using radiofrequency technology [20,21]. The GSMA estimates that the number of IoT connections will reach 25 billion worldwide by 2025 [22,23]. IoT is a collection of devices with embedded systems connected to the internet’s telecommunications network with the ability to generate and send data without the need for human interaction. IoT consists of machine-to-machine (M2M) networks, in which intelligent devices communicate with one another and make independent decisions based on created and transmitted data [24,25].
|Wireless Communication||Transmission Area||Bandwidth||Transmission Rate||Network Topology||Authentication|
|Sigfox||9.5 km||868 MHz (Europe); 915 MHz (US)||100 bps||Star||Private key signature Encryption|
|NB-IoT||15 km||LTE bands||250 kbps||NA||3GPP S3 security|
|LoRa||7.2 km||433 MHz (Asia), 868 MHz (Eu) and 915 MHz (US)||0.25–5.5 kpbs||Star of stars||Distinctive key distribution,|
|BLE||150 m||2.4 GHz||1 Mbps||Star, bus||Secure pairing before the key exchange|
|Zigbee||30 m||2.4 GHz||250 kbps||Star, Peer 2Peer (P2P), mesh, tree,||AES-128b, Network key|
|Wi-Fi||100 m||2.4 GHz||54 Mb/s||Point-to-hub|
|MQTT||Machine 2 Machine (M2M)||2.4 GHz||2 Mbps||TLS/SSL|
- AI: AI is the process of creating intelligent devices out of massive amounts of data . Human-like tasks are performed by systems that learn from previous learning and experiences. Human efforts are improved in terms of speed, precision, and efficacy. To create machines that can make judgments on their own, AI uses complicated algorithms and methodologies. Figure 6 illustrates the subsets of AI, and they are machine learning (ML), deep learning (DL), expert systems, natural language processing (NLP), robotics, vision, and speech recognition . ML is a branch of AI that offers intelligence to computers by allowing them to learn from their experiences without being explicitly programmed .
- Edge computing: In IoT, many computing paradigms such as edge computing, cloud computing, and mobile ad hoc cloud (MAC) are employed to provide various services based on the application requirements . Real-time delay-sensitive applications cannot accept prolonged delays generated by a wide area network during IoT installation. IoT devices have limited processing power, making them inadequate for computationally intensive workloads . Edge computing is a decentralized computing platform that offers cloud computing capabilities to IoT devices at the network edge. However, IoT devices with limited resources can enhance their capabilities by utilizing the resources of edge servers. Latency minimization, network management, cost optimization, energy management, resource management, and data management are the advantages of edge computing implementation .
- UAV: The technology can be customized and used for fast mobile applications that can be employed in a variety of smart city applications. UAV technology is developed to match smart city criteria and functions that will productively connect UAVs to smart cities . By enabling a UAV with monitoring sensors, cameras, as well as software, we can perform smart city infrastructure inspection and control applications . Furthermore, one of the emerging fields of UAVs is their involvement in smart city applications, which results in a variety of benefits. UAVs, as compared to manned planes, can be more cost-effective. They are more adaptable and can function in a variety of environments and settings, including those that are difficult or dangerous to people. Traffic monitoring and management, health emergency services, security and crowd monitoring, UAV-based infrastructure inspections, agriculture management, environmental monitoring, tourism support, and UAV-aided wireless communications are the applications of UAVs .
- Sensing: Sensing technology enables the establishment of an ecosystem for connecting the physical and virtual worlds. Sensors collect signals, convert them to digital data, and process them. Further, the information on the present status of the physical environment is converted into usable data.
- IoT: IoT is built on the integration of many standards and supporting technologies such as sensors, communication protocol, storage, computational, and other capabilities. At present, the advancement in sensor technology and communication protocol empowers us to implement IoT in various applications for real-time analysis. The application of IoT in the BIPV system enables us to monitor and control the BIPV system from any remote location in real-time.
- AI: The implementation of IoT generates a large amount of sensor and visual data. The evolution of AI simulates human intelligent behavior with computers and trains computers to learn human characteristics such as judgment, learning, and decision-making. The application of AI with IoT enables us to predict events based on real-time data.
- Data Analytics: Advanced data analytics reveals computing approaches, allowing for the interpretation of data. It is critical for converting huge data into usable data for better decision-making.
- Edge Computing: Data is currently being increasingly generated at the edge of the network, and it would be more effective to handle the data there as well. Edge computing refers to the technologies that enable computation to be conducted at the network’s edge, on downstream data for cloud services and upstream data for IoT services.
- Drone Technology: A drone is a type of unmanned aircraft. A drone is effectively a flying robot that can be operated remotely or autonomously by software-controlled flight patterns in its embedded systems, which work in concert with onboard sensors and GPS.
- Robotics Technology: An autonomous robot is a system that operates in an unpredictable and partially unknown environment, and it is able to navigate without interruption and avoid any obstacles placed within the mobility confinement. The integration of IoT with robotics enables the realization of the robotics-based automated system for different applications in the BIPV system.
- Human to BIPV interaction: The digitalization of the BIPV system is relevant to controlling the performance of the system and rectifying the issues. Interaction of humans with the BIPV system is important to manage its performance.
4. Measures to Overcome the Challenges
4.1. High Temperature of BIPV System
4.2. Faults Occurrence in the BIPV System
4.3. Dust Accumulation on BIPV
5. Future Directions and Research Challenges
Next Generation Computing for Digitalization of the Photovoltaic Integration
- Future directions
- ML and DL for image enhancement for flaws detection in real-time image data
- Edge computing to implement DL for Intelligent BIPV data analytics
- Fog computing for 6G assisted IoT network in BIPV
- Edge computing integration in UAV for intelligent automation and detection
- Augmented Reality, Virtual Reality, and Digital Twins for virtual BIPV system
- Research challenges
- The implementation of AI in edge computing has significant advantages, along with a few challenges that require the focus of researchers, such as computational power, memory, security, and power management of the edge devices. In the area of BIPV, the energy generated by the edge devices will be based on renewable energy, however, the life of edge devices concerning the performance of computational activities is challenging because the replacement of edge devices within a short period also increases the infrastructure and adds another operating cost.
- Along with the life of the edge devices, another significant parameter that needs to be considered is the security and privacy of the data. In the context of the BIPV, the building structure data, and the panel details with location are shared on the edge network. The data is comprised during transmission from the edge to the cloud server. Recently, blockchain has been integrated with edge computing to achieve secure authentication and collaboration with trusted distributers [77,78].
- To perform the high computational activities with AI, there is a need for additional computational hardware and this hardware consumes a lot of energy to perform the computational activities . The reduction of computation immediately reduces energy consumption, where there is a need for a deeper understanding of AI computation with battery management measures, such as CPU throttling and sensor hardware modifications, which is an attractive research avenue . Changes in input data, whether detected in software or hardware, can help reduce the frequency of AI runs and overall energy consumption. This challenge enables us to carry out research for better understanding of hardware chips (GPUs and TPUs) during the computation process.
Conflicts of Interest
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|Current||Detects and converts current to an easily measured output voltage, which is proportional to the current through the measured path.|
|Voltage||The input is the voltage and the output is its analog voltage signal, a current signal, or an audible signal.|
|Optical||Measures the physical quantity of light rays and converts this into electrical signals which can be easily read by the instruments.|
|Radiometric||Measures the UV irradiance or illuminance with the radiometer. In the case of non-vertical irradiation, the diffusers inside the sensors ensures the cosine correction.|
|IR||Emits or detects infrared radiation in its surroundings and also measures the heat emitted by the objects.|
|Lux meter||Converts the amount of light that falls on the photodiode into a current form. The amount of current measured will give the approximate value of lux radiation falling on the surface.|
|Pyranometer||Measures the potential difference developed due to the temperature difference between two surfaces of the thermopile. The gradient of temperature is used to measure the sum of solar radiation.|
|Anemometer||Measures the input power required to maintain the temperature of the hot wire cooled down due to the flow of air. The input power is utilized as the measurement of airspeed.|
|Temperature||Measures the temperature through an electrical signal using the thermocouple. The thermocouple will change electrical resistance indirectly proportionally to changes in the temperature.|
|Humidity||Measures the change in electrical permittivity of dielectric material and change in the resistivity value of this resistive material for predicting the change in humidity.|
|||IoT||Temperature, radiance, humidity, pressure, wind speed, etc.||Instant power and energy|
|||IoT||Temperature sensor, voltage transducers, current transducers,||Instant power and energy|
|||IoT||Current, voltage, irradiation, and temperature.||Solar radiation, instant power, and energy|
|||IoT||Current sensor temperature and humidity sensor, optical dust sensor||Instant power level, and weather condition|
|||Drones, IoT||Radiometric sensors, thermographic camera||Dust accumulation on PV modules|
|||Drones, image processing||IR camera||Dust accumulation|
|||Robotic cleaning||voltage and current sensor||Dust accumulation|
|||Robotic cleaning||Illuminance sensor, voltage, and current sensor; temperature and humidity sensor, dust sensor.||Dust accumulation|
|||IoT, Cloud||Temperature sensor, voltage transducers, current transducers,||Dust accumulation, open circuit, shading effect|
|||Drones, IoT, image processing||voltage and current sensor; an optical and thermal camera||Instant power level, faults, weather condition|
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Singh, D.; Akram, S.V.; Singh, R.; Gehlot, A.; Buddhi, D.; Priyadarshi, N.; Sharma, G.; Bokoro, P.N. Building Integrated Photovoltaics 4.0: Digitization of the Photovoltaic Integration in Buildings for a Resilient Infra at Large Scale. Electronics 2022, 11, 2700. https://doi.org/10.3390/electronics11172700
Singh D, Akram SV, Singh R, Gehlot A, Buddhi D, Priyadarshi N, Sharma G, Bokoro PN. Building Integrated Photovoltaics 4.0: Digitization of the Photovoltaic Integration in Buildings for a Resilient Infra at Large Scale. Electronics. 2022; 11(17):2700. https://doi.org/10.3390/electronics11172700Chicago/Turabian Style
Singh, Digvijay, Shaik Vaseem Akram, Rajesh Singh, Anita Gehlot, Dharam Buddhi, Neeraj Priyadarshi, Gulshan Sharma, and Pitshou N. Bokoro. 2022. "Building Integrated Photovoltaics 4.0: Digitization of the Photovoltaic Integration in Buildings for a Resilient Infra at Large Scale" Electronics 11, no. 17: 2700. https://doi.org/10.3390/electronics11172700